7 citations · 9 across the 13 of their papers we have counts for
6 papers · 1 filter
Efficient Cloud-Edge-Device Query Execution Based on Collaborative Scan Operator
Chunyu Zhao, Hongzhi Wang, Kaixin Zhang +7
In cloud-edge-device (CED) collaborative query (CQ) processing, by leveraging CED collaboration, the advantages of both cloud computing and edge resources can be fully integrated.…
DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation
Kaixin Zhang, Hongzhi Wang, Ziqi Li +4
Research on learned cardinality estimation has made significant progress in recent years. However, existing methods still face distinct challenges that hinder their practical deplo…
CONCERTO: Complex Query Execution Mechanism-Aware Learned Cost Estimation
Kaixin Zhang, Hongzhi Wang, Kunkai Gu +4
With the growing demand for massive data analysis, many DBMSs have adopted complex underlying query execution mechanisms, including vectorized operators, parallel execution, and dy…
Updateable Data-Driven Cardinality Estimator with Bounded Q-error
Yingze Li, Xianglong Liu, Hongzhi Wang +2
Modern Cardinality Estimators struggle with data updates. This research tackles this challenge within single-table. We introduce ICE, an Index-based Cardinality Estimator, the firs…
QCFE: An efficient Feature engineering for query cost estimation
Yu Yan, Hongzhi Wang, Junfang Huang +5
Query cost estimation is a classical task for database management. Recently, researchers apply the AI-driven model to implement query cost estimation for achieving high accuracy. H…
Duet: efficient and scalable hybriD neUral rElation undersTanding
Kaixin Zhang, Hongzhi Wang, Yabin Lu +4
Learned cardinality estimation methods have achieved high precision compared to traditional methods. Among learned methods, query-driven approaches have faced the workload drift pr…